In [1]}, the authors present a deep CNN model (DeTraC) that indexes COVID-19 chest X-ray images. DeTraC can compact any abnormalities in the image dataset using a class decomposition mechanism. DeTraC model consists of three phases. In the first phase backbone pre-trained CNN model of DeTraC is trained to fetch the deep local features of every image. In the second phase, a sophisticated gradient descent optimization process is used for training. In the last phase, the class composition layer is utilized to improve the final classification of Images. DeTraC model is trained and tested using a combination of two datasets (80 CXR images for normal patients and 116 for COVID-19 patients). The experimental results in [1]} show that the DeTraC model exhibits a promising accuracy of 93.1% (with a sensitivity of 100%) for identifying COVID-19 X-ray images from normal and severe acute respiratory syndrome cases. However, the small dataset used for training and testing the model degrade its performance in complex scenarios.
